An effective battery management scheme for wind energy systems using multi Kernel Ridge regression algorithm

Abstract

Abstract Battery Energy Storage (BES) systems are adequate alternative for any Wind Power Generation System (WPGS) for achieving greater operational flexibility by compensating the generation volatility. An efficient Local Energy Management (LEM) for Wind/ Battery Energy Storage (BES) system is described in this chapter to reduce the prediction error and to improve associated battery life. Error in prediction impacts on DG control reference & system stability for local energy management. BES life degrades by prediction error in terms of battery power loss and temperature. A Doubly-Fed Induction Generator (DFIG) based WPGS is considered as primary Distributed Generation (DG), where DC link voltage stability challenges due to wind speed volatility, as well as prediction error is addressed with Lithium-ion (Li-ion) BES stacks. A new online Multi Kernel Ridge Pseudo Inverse Neural Network (MK-RPINN) algorithm is proposed to obtain an impactful decrease in prediction error. A new secondary controller is proposed here for addressing temperature effect of battery. A Model Reference (MR) based battery temperature plan, linked to Rule based switching characteristics of battery stacks with temperature tolerance, is being incorporated in the proposed secondary controller. The effectiveness of this model is represented in various observations through TMS320 C6713 and MATLAB platform.